• Journal of the Korean Society for information Management
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• v.22 no.3 s.57
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• pp.261-287
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• 2005

The purpose of this paper is to explore the ways to improve the performance of SVM (Support Vector Machines) text classifier using inter-document similarities. SVMs are powerful machine learning systems, which are considered as the state-of-the-art technique for automatic document classification. In this paper text categorization via SVMs approach based on feature representation with document vectors is suggested. In this approach, document vectors instead of index terms are used as features, and vector similarities instead of term weights are used as feature values. Experiments show that SVM classifier with document vector features can improve the document classification performance. For the sake of run-time efficiency, two methods are developed: One is to select document vector features, and the other is to use category centroid vector features instead. Experiments on these two methods show that we can get improved performance with small vector feature set than the performance of conventional methods with index term features.

• Proceedings of the IEEK Conference
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• 2000.06d
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• pp.198-201
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• 2000

We propose a method for analyzing the document structure. This method consists of two processes, segmentation and classification. The segmentation first divides a low resolution image, and then finely splits the original document image using projection profiles. The classification deterimines each segmented region as text, line, table or image. An experiment with 238 documents images shows that the segmentation accuracy is 99.1% and the classification accuracy is 97.3%.

• The KIPS Transactions:PartB
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• v.11B no.7 s.96
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• pp.849-854
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• 2004

This paper proposes an algorithm to enhance the execution time of kNN in the document classification. The proposed algorithm is to enhance the execution time by minimizing the computing cost of the similarity between two documents by using the list of pairs, while the conventional kNN uses the iist of pairs. The 1ist of pairs can be obtained by applying the matrix transposition to the list of pairs at the training phase of the document classification. This paper analyzed the proposed algorithm in the time complexity and compared it with the conventional kNN. And it compared the proposed algorithm with the conventional kNN by using routers-21578 data experimentally. The experimental results show that the proposed algorithm outperforms kNN about $90{\%}$ in terms of the ex-ecution time.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.2
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• pp.81-86
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• 2016

Term weighting is a popular technique that effectively weighs the term features to improve accuracy in document classification. While several successful term weighting algorithms have been suggested, none of them appears to perform well consistently across different data domains. In this paper we propose several reasonable methods to combine different term weight vectors to yield a robust document classifier that performs consistently well on diverse datasets. Specifically we suggest two approaches: i) learning a single weight vector that lies in a convex hull of the base vectors while minimizing the class prediction loss, and ii) a mini-max classifier that aims for robustness of the individual weight vectors by minimizing the loss of the worst-performing strategy among the base vectors. We provide efficient solution methods for these optimization problems. The effectiveness and robustness of the proposed approaches are demonstrated on several benchmark document datasets, significantly outperforming the existing term weighting methods.

• Journal of the Korea Society of Computer and Information
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• v.23 no.10
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• pp.103-109
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• 2018

In this paper, we propose a dynamic document classification method which breaks away from existing document classification method with artificial categorization rules focusing on suppliers and has changing categorization rules according to users' needs or social trends. The core of this dynamic document classification method lies in the fact that it creates classification criteria real-time by using topic modeling techniques without standardized category rules, which does not force users to use unnecessary frames. In addition, it can also search the details through the relevance analysis by calculating the relationship between the words that is difficult to grasp by word frequency alone. Rather than for logical and systematic documents, this method proposed can be used more effectively for situation analysis and retrieving information of unstructured data which do not fit the category of existing classification such as VOC (Voice Of Customer), SNS and customer reviews of Internet shopping malls and it can react to users' needs flexibly. In addition, it has no process of selecting the classification rules by the suppliers and in case there is a misclassification, it requires no manual work, which reduces unnecessary workload.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.311-318
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• 2002

Document image layout analysis contains the process to segment document image into detailed regions and the process to classify the segmented regions into text, picture, table or etc. In the region classification process, the size of a region, the density of black pixels, and the complexity of pixel distribution are the bases of region classification. But in case of picture, the ranges of these bases are so wide that it's difficult to decide the classification threshold between picture and others. As a result, the picture has a higher region classification error than others. In this paper, we propose document image layout analysis method which has a better performance for the picture and text region classification than that of previous methods including commercial softwares. In the picture and text region classification, median filter is used in order to reduce the influence of the size of a region, the density of black pixels, and the complexity of pixel distribution. Futhermore the classification error is corrected by the use of region expanding filter and constrained conditions.

• Journal of Computing Science and Engineering
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• v.2 no.2
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• pp.109-136
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• 2008

In this survey paper, we discuss biomedical ontologies and major text mining techniques applied to biomedicine and healthcare. Biomedical ontologies such as UMLS are currently being adopted in text mining approaches because they provide domain knowledge for text mining approaches. In addition, biomedical ontologies enable us to resolve many linguistic problems when text mining approaches handle biomedical literature. As the first example of text mining, document clustering is surveyed. Because a document set is normally multiple topic, text mining approaches use document clustering as a preprocessing step to group similar documents. Additionally, document clustering is able to inform the biomedical literature searches required for the practice of evidence-based medicine. We introduce Swanson's UnDiscovered Public Knowledge (UDPK) model to generate biomedical hypotheses from biomedical literature such as MEDLINE by discovering novel connections among logically-related biomedical concepts. Another important area of text mining is document classification. Document classification is a valuable tool for biomedical tasks that involve large amounts of text. We survey well-known classification techniques in biomedicine. As the last example of text mining in biomedicine and healthcare, we survey information extraction. Information extraction is the process of scanning text for information relevant to some interest, including extracting entities, relations, and events. We also address techniques and issues of evaluating text mining applications in biomedicine and healthcare.

• Journal of Korea Multimedia Society
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• v.14 no.6
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• pp.730-741
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• 2011

With the development of the internet and computer technology, the amount of information through the internet is increasing rapidly and it is managed in document form. For this reason, the research into the method to manage for a large amount of document in an effective way is necessary. The conventional document categorization method used only the keywords of related documents for document classification. However, this paper proposed keyword extraction method of based on association rule. This method extracts a set of related keywords which are involved in document's category and classifies representative keyword by using the classification rule proposed in this paper. In addition, this paper proposed the preprocessing method for efficient keywords creation and predicted the new document's category. We can design the classifier and measure the performance throughout the experiment to increase the profile's classification performance. When predicting the category, substituting all the classification rules one by one is the major reason to decrease the process performance in a profile. Finally, this paper suggested automatically categorizing plan which can be applied to hierarchical category architecture, extended from simple category architecture.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.10
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• pp.120-129
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• 1996

This paper presents an efficient block segmentation and classification using the edge information of the document image. We extract four prominent features form the edge gradient and orientaton, all of which, and thereby the block clssifications, are insensitive to the background noise and the brightness variation of of the image. Using these four features, we can efficiently classify a document image into the seven categrories of blocks of small-size letters, large-size letters, tables, equations, flow-charts, graphs, and photographs, the first five of which are text blocks which are character-recognizable, and the last two are non-character blocks. By introducing the clumn interval and text line intervals of the document in the determination of th erun length of CRLA (constrained run length algorithm), we can obtain an efficient block segmentation with reduced memory size. The simulation results show that the proposed algorithm can rigidly segment and classify the blocks of the documents into the above mentioned seven categories and classification performance is high enough for all the categories except for the graphs with too much variations.

• Journal of Korea Society of Industrial Information Systems
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• v.13 no.5
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• pp.22-28
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• 2008

This paper aims to offer a solution based on semantic document classification to improve e-Government utilization and efficiency for people using their own information retrieval system and linguistic expression. Generally, semantic document classification method is an approach that classifies documents based on the diverse relationships between keywords in a document without fully describing hierarchial concepts between keywords. Our approach considers the deep meanings within the context of the document and radically enhances the information retrieval performance. Concept Weight Document Classification(CoWDC) method, which goes beyond using existing keyword and simple thesaurus/ontology methods by fully considering the concept hierarchy of various concepts is proposed, experimented, and evaluated. With the recognition that in order to verify the superiority of the semantic retrieval technology through test results of the CoWDC and efficiently integrate it into the e-Government, creation of a thesaurus, management of the operating system, expansion of the knowledge base and improvements in search service and accuracy at the national level were needed.